![]() We have documented the usages of other options extensively in the S3 Select documentation section. SQL – CREATE TABLE foo USING ".3select" LOCATION s3://bucket/filename OPTIONS().Dataframe API – (".3select").option(“fileFormat”, “csv”).load().With this new data source, S3 Select can be used out of the box both with data frames as well as SQL. Qubole added a new data source for S3 Select in Spark with the recent Qubole Data Platform release. The following section explains how to create data frames or create tables manually using S3 Select on top of CSV or JSON data sources. For instance in query “SELECT * FROM foo”, the foo table would not need S3 Select.īut sometimes users need more control over how they access the data, so we also provide ways to create a data source on top of S3 Select manually. If S3-backed tables in a query do not require any column projections or row filtering, then they are not optimized as they are already better off with a normal S3 read.Compressed data is currently not supported.The data format must be either CSV or JSON.Data types used for the columns must be supported by Amazon S3 Select.Below are the few requirements that the query should satisfy to get converted to S3 Select: In the optimization phase, the newly added rules try to optimize the user’s queries with S3 Select if possible. In order to achieve AutoConversion with S3 Select, we added rules to Spark SQL’s optimizer (Catalyst). SQL optimizers in general look at the user’s queries and try to optimize the queries for the best performance. We call it AutoConversion, and we will go into the details in the next section. We went one step further by automatically optimizing existing CSV/JSON tables or data frames using S3 Select without any change in the application code. Typically, it would be hard to change the existing code or recreate new tables with S3 Select. This drastically reduces the network I/O happening for needle-in-the-haystack kinds of queries, thereby speeding up the query. Whereas in Figure 2 with S3 Select optimization turned on, Spark sends the S3 Select SQL based on the application code and gets back only the filtered portion of data from S3 Select. In Figure 1, without S3 Select optimization, Spark reads file after file and filters the data based on the predicate. The above diagrams illustrate how Spark interacts with the S3 Select service at a logical level. It can automatically convert existing CSV or JSON-based S3-backed tables to use S3 Select by pushing filters and columns used in the user’s query. Spark on Qubole supports using S3 Select to read S3-backed tables created on top of CSV or JSON files. Using the Datasource abstraction, we built a new data source to integrate S3 Select with Spark on Qubole. Spark on Qubole – S3 Select integrationĪpache Spark supports plugging in a new data source to the engine using an abstraction called Datasource. We are happy to announce that Apache Spark on Qubole can now automatically use the S3 Select service whenever applicable to speed up queries (meaning there’s no need for an application code change). With this format, we would read only the necessary data, which can drastically cut down on the amount of network I/O required.Īt Qubole, we are looking at various ways to improve the overall query time. ![]() Cloud object stores are popular mainly because they are infinitely scalable, cheaper, and more fault-tolerant, though they add latencies to various traditional file system operations.Īmazon S3 Select is a service from Amazon S3 that supports the retrieval of a subset of data from the whole object based on the filters and columns used for file formats like CSV, JSON, etc. SQL-on-Hadoop engines like Apache Spark, Apache Hive, and Presto are all processing huge amounts of data stored in the cloud object stores. ![]() to store the data for analytical purposes. With the advent of the cloud, data lakes built on the cloud-primarily use object storage like Amazon S3, Google Cloud Storage, Azure Blob Storage, etc.
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